title	emoji	colorFrom	colorTo	sdk	app_port
LeafLense - AI-Powered Agricultural Assistant	🌱	green	blue	docker	7860

🌱 LeafLense - AI-Powered Agricultural Assistant

LeafLense is an advanced agricultural intelligence platform that combines machine learning, computer vision, and AI agents to provide comprehensive farming solutions. The platform offers plant disease detection, fertilizer recommendations, crop yield predictions, and proactive farming alerts through an intelligent agent system.

🌟 Key Features

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🌱 LeafLense - AI-Powered Agricultural Assistant

LeafLense is an advanced agricultural intelligence platform that combines machine learning, computer vision, and AI agents to provide comprehensive farming solutions. The platform offers plant disease detection, fertilizer recommendations, crop yield predictions, and proactive farming alerts through an intelligent agent system.

🌟 Key Features

🔍 Plant Disease Detection

• AI-Powered Diagnosis: Upload plant leaf images for instant disease identification
• 38+ Disease Classes: Supports detection across multiple crops (Apple, Tomato, Corn, Grape, etc.)
• Treatment Recommendations: Provides organic and chemical treatment options
• Confidence Scoring: Reliable predictions with confidence percentages

🌾 FarmAgent - Proactive Farming Assistant

• Real-time Weather Integration: Continuous weather monitoring and analysis
• Risk Assessment Engine: Predicts disease and pest risks based on environmental conditions
• Automated Alerts: Daily notifications via WhatsApp for farming recommendations
• Farmer Management: Complete farmer profile and location management system
• AI Chat Interface: Contextual farming advice through intelligent conversations

🧪 Fertilizer Recommendation System

• Soil Analysis: NPK (Nitrogen, Phosphorus, Potassium) level optimization
• Environmental Factors: Considers temperature, humidity, and moisture
• Crop-Specific: Tailored recommendations for different crop types
• Natural Language Processing: Accept recommendations via plain text descriptions

📊 Crop Yield Prediction

• Data-Driven Insights: Predict crop yields based on historical and current data
• Market Intelligence: Price prediction and market trend analysis
• Decision Support: Help farmers optimize planting and harvesting decisions

💬 Intelligent Chatbot

• Multi-language Support: Communicate in local languages
• Voice Integration: Speech-to-text and text-to-speech capabilities
• Context-Aware: Understands farmer-specific context and location
• 24/7 Availability: Always available farming assistance

🏗️ System Architecture

Backend (FastAPI)

backend/
├── main.py                 # Main FastAPI application
├── FarmAgent/              # Proactive farming agent
│   ├── app/
│   │   ├── agents/         # Weather, risk, chat agents
│   │   ├── clients/        # Firebase, weather API clients
│   │   └── utils/          # Utility functions
│   └── routes.py           # FarmAgent API endpoints
├── Plant_Disease/          # Disease detection module
│   ├── routes.py           # Disease detection endpoints
│   └── trained_model_savedmodel/  # Pre-trained ML model
├── FertilizerSuggestor/    # Fertilizer recommendation
│   └── routes.py           # Fertilizer API endpoints
└── PricePrediction/        # Crop price prediction
    └── app.py              # Price prediction service

Frontend (React + Vite)

frontend/
├── src/
│   ├── components/         # Reusable UI components
│   │   └── layout/         # Layout components
│   ├── pages/              # Application pages
│   ├── config/             # Configuration files
│   └── assets/             # Static assets
├── public/                 # Public assets
└── package.json            # Dependencies and scripts

🔧 System Requirements

Backend Requirements

• Python: 3.8+
• FastAPI: 0.115.0+
• TensorFlow: 2.17+ (for disease detection)
• Firebase Admin SDK: For database and authentication
• Google Generative AI: For AI-powered responses

Frontend Requirements

• Node.js: 18+
• React: 18.3.1+
• Vite: 6.0.1+
• Tailwind CSS: For styling

External Services

• Firebase: Database and authentication
• Google Gemini API: AI language model
• OpenWeather API: Weather data
• WhatsApp Business API: Alert notifications

🚀 Installation & Setup

1. Clone the Repository

git clone https://github.com/yourusername/LeafLense.git
cd LeafLense

2. Backend Setup

Install Python Dependencies

cd backend
pip install -r requirements.txt

Environment Configuration

Create a .env file in the backend/ directory:

# Google APIs
GOOGLE_API_KEY=your_google_api_key_here
GEMINI_API_KEY=your_gemini_api_key_here

# OpenWeather API
OPENWEATHER_API_KEY=your_openweather_api_key

# Application Settings
PORT=8000
ENVIRONMENT=development

# Database
DATABASE_URL=your_database_url_if_needed

Firebase Setup

1. Create a Firebase project at Firebase Console
2. Enable Firestore Database
3. Generate a service account key
4. Save the key as serviceAccountKey.json in the backend/ directory

Run Backend Server

# From the backend directory
python main.py

The backend server will start at: http://localhost:8000

3. Frontend Setup

Install Node.js Dependencies

cd frontend
npm install

Environment Configuration

Create a .env file in the frontend/ directory:

VITE_API_BASE_URL=http://localhost:8000
VITE_FIREBASE_API_KEY=your_firebase_config
VITE_FIREBASE_AUTH_DOMAIN=your_firebase_config
VITE_FIREBASE_PROJECT_ID=your_firebase_config
VITE_FIREBASE_STORAGE_BUCKET=your_firebase_config
VITE_FIREBASE_MESSAGING_SENDER_ID=your_firebase_config
VITE_FIREBASE_APP_ID=your_firebase_config

Run Frontend Development Server

# From the frontend directory
npm run dev

The frontend will be available at: http://localhost:3000

📡 API Endpoints

FarmAgent Endpoints

• GET /farm/farmagent/ - Service status
• POST /farm/farmagent/run-now - Trigger immediate pipeline execution
• POST /farm/farmagent/farmers - Register new farmer
• GET /farm/farmagent/farmers - Get all farmers
• GET /farm/farmagent/alerts - Get all alerts
• GET /farm/farmagent/alerts/{farmer_id} - Get farmer-specific alerts
• POST /farm/farmagent/api/chat - Chat with AI assistant

Plant Disease Detection

• POST /plant/disease/predict - Upload image for disease detection

Fertilizer Recommendation

• POST /fertilizer/predict - Get fertilizer recommendation (structured data)
• POST /fertilizer/predict_from_text - Get recommendation from natural language

🏃‍♂️ Running the Application

Development Mode

1. Start Backend:

   cd backend && python main.py

2. Start Frontend:

   cd frontend && npm run dev

3. Access Application: Open http://localhost:3000 in your browser

Production Build

Backend (using Gunicorn)

cd backend
gunicorn -w 4 -k uvicorn.workers.UvicornWorker main:app --bind 0.0.0.0:8000

Frontend (Build & Serve)

cd frontend
npm run build
npm run preview

🔧 Configuration

Model Setup

The plant disease detection requires a trained TensorFlow model:

1. Ensure the model is in backend/Plant_Disease/trained_model_savedmodel/
2. The model should support 38 plant disease classes
3. Expected input shape: (None, 128, 128, 3)

Firebase Configuration

1. Set up Firestore collections:
   • farmers: Farmer profiles and locations
   • alerts: Generated alerts and recommendations
   • chat_messages: Chat history

Weather Integration

• Register at OpenWeatherMap
• Add your API key to the .env file
• The system fetches weather data every 6 hours

📱 Features Walkthrough

Disease Detection

1. Navigate to /disease-detection
2. Upload a clear image of a plant leaf
3. Get instant diagnosis with confidence score
4. Receive treatment recommendations

Fertilizer Recommendations

1. Go to /fertilizer
2. Input soil conditions and crop information
3. Receive NPK recommendations
4. Alternative: Use natural language input

Farm Monitoring

1. Register farmers through /farm/farmagent/farmers
2. System automatically monitors weather conditions
3. Receives daily alerts at 6:00 AM IST
4. Chat interface available for real-time advice

🚀 Deployment

Docker Deployment

# Backend Dockerfile
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Cloud Deployment Recommendations

• Backend: Deploy on Google Cloud Run, AWS Lambda, or Heroku
• Frontend: Deploy on Vercel, Netlify, or AWS S3 + CloudFront
• Database: Use Firebase Firestore or MongoDB Atlas
• File Storage: Google Cloud Storage or AWS S3

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit changes: git commit -m 'Add amazing feature'
4. Push to branch: git push origin feature/amazing-feature
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📧 Support

For support, email [email protected] or join our Discord community.

🙏 Acknowledgments

• TensorFlow team for the machine learning framework
• Google for Gemini AI and Firebase services
• OpenWeatherMap for weather data
• The agricultural research community for disease datasets

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